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CAISEr: Comparing Algorithms with Iterative Sample-size Estimation in R

Felipe Campelo (fcampelo@ufmg.br) and Fernanda Takahashi (fernandact@ufmg.br)
Operations Research and Complex Systems Laboratory - ORCS Lab
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil


Implementation of R package CAISEr, with routines for automatically determining the sample size needed for performing comparative experiments with algorithms.

To install the most up-to-date version directly from Github, simply type:

library(devtools)
devtools::install_github("fcampelo/CAISEr")

The most recent CRAN release of the package is also available for installation directly from the R prompt, using:

install.packages("CAISEr")

For instructions and examples of use, please take a look at the vignette Adapting Algorithms for CAISEr, and at the package documentation, particularly functions run_experiment() and run_nreps2().

Please send any bug reports, questions, suggestions, chocolate (to Fernanda) or beers (to Felipe - we can always hope!) directly to the package authors listed at the top of this document.

Cheers,
Felipe

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Version

Install

install.packages('CAISEr')

Monthly Downloads

236

Version

0.3.3

License

GPL-2

Maintainer

Felipe Campelo

Last Published

July 24th, 2018

Functions in CAISEr (0.3.3)

se_boot

Bootstrap standard errors
summary.CAISEr

summary.CAISEr
my.SANN

Simulated annealing (for testing/examples) Adapted from stats::optim
boot_sdm

Bootstrap the sampling distribution of the mean
plot.CAISErPowercurve

plot.caiser.powercurve
se_param

Parametric standard errors
print.CAISEr

print.CAISEr
run_experiment

Run a full experiment
dummyinstance

Dummy instance (for testing only) - a function that does nothing and returns nothing
summary.CAISErPowercurve

summary.CAISErPowercurve
get_observations

Run an algorithm on a problem.
calc_phi

Calculates the sample estimator of (simple or percent) differences
calc_power_curve

Calculate the power curve for an experiment
TSP.dist

TSP instance generator (for testing/examples) Adapted from stats::optim
calc_ropt

Calculates the optimal ratio of sample sizes
calc_se

Calculates the standard error for simple and percent differences
consolidate.partial.results

Consolidate results from partial files
calc_instances

Calculates number of instances for the comparison of two algorithms
dummyalgo

Dummy algorithm routine to test the sampling procedures
calc_nreps2

Determine sample sizes for a pair of algorithms on a problem instance